Federated learning (FL) has recently emerged as an important and promising learning
scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets …

Communication is widely known as the primary bottleneck of federated learning, and
quantization of local model updates before uploading to the parameter server is an effective …

Federated Learning (FL) algorithms commonly sample a random subset of clients to address
the straggler issue and improve communication efficiency. While recent works have …

This work presents a new method for enhancing communication efficiency in stochastic
Federated Learning that trains over-parameterized random networks. In this setting, a binary …

The conventional model aggregation-based federated learning (FL) approaches require all
local models to have the same architecture and fail to support practical scenarios with …

Federated learning (FL) has emerged as a distributed machine learning (ML) technique that
can protect local data privacy for participating clients and improve system efficiency. Instead …

In this paper, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In particular, in the considered model, wireless users perform an …

We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …